lets_plot.geom_vline¶
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lets_plot.geom_vline(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, xintercept=None, **other_args)¶ Add a straight vertical line to the plot.
- Parameters
mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.
data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.
stat (str, default=’identity’) – The statistical transformation to use on the data for this layer, as a string.
position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.
show_legend (bool, default=True) – False - do not show legend for this layer.
sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.
tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.
xintercept (float) – The value of x at the point where the line crosses the x axis.
other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.
- Returns
Geom object specification.
- Return type
LayerSpec
Note
geom_hline() understands the following aesthetics mappings:
xintercept : line x-intercept.
alpha : transparency level of a layer. Understands numbers between 0 and 1.
color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.
size : lines width. Defines line width.
linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.
Examples
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from lets_plot import * LetsPlot.setup_html() ggplot() + geom_vline(xintercept=0)
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import numpy as np import pandas as pd from lets_plot import * LetsPlot.setup_html() n = 100 classes = ['a', 'b', 'c'] np.random.seed(42) x = np.random.normal(size=n) y = np.random.normal(size=n) c = np.random.choice(classes, size=n) df = pd.DataFrame({'x': x, 'y': y, 'c': c}) bounds_df = pd.DataFrame([(cl, df[df.c == cl].x.max()) for cl in classes], \ columns=['c', 'xmax']) ggplot() + \ geom_vline(aes(xintercept='xmax', color='c'), \ data=bounds_df, size=.2, linetype='longdash') + \ geom_point(aes(x='x', y='y', color='c'), data=df)